Personalized medicine is considered a branch of healthcare that co-opts a proactive approach to patient care, wherein the disease is detected early, and the focus on illness is prevention
As opposed to reactive healthcare, wherein detecting of disease is made based on symptoms presenting in the patient after the disease has already significantly progressed and treatments are less effective. Early detection can occur through genetic testing that looks for unique variations in the genome, the sum total of all heritable DNA in an individual. The impact of personalized medicine to date has mainly been in the areas of genetic testing and precision oncology.
Personalized medicine has been described in terms of being no less than revolutionary and represents a paradigm shift in healthcare. Its rise is compared to another moment in medical history when the Flexner Report was released in 1910. This report caused a transformation in healthcare, medical practice and medical education, in which medicine became known by the characteristics by which it is known in modern-day society: evidence-based treatment as a result of rigorous randomized trials with medical diagnosis made on the basis of the scientific principles of pathology, physiology, biochemistry and microbiology inculcated through academic and clinical training. Personalized medicine promises to be as disruptive, with healthcare shifting from a one-size-fits-all approach to individualized treatment based on the unique genomic, metabolomic, proteomic, epigenomic and microbiome aspects of the patient.
Arguments in support of the implementation of personalized medicine run the gamut from promoting scientific advancement and health outcomes or leading to more efficient care in the form of digital health (see below). Other arguments are economically based in that personalized medicine may mitigate the healthcare crisis through its emphasis on preventive care and streamlining of clinical trials (see below), thus curtailing attendant costs in drug development. In terms of its economic impact, on recent economic report stated that “[a] conservative estimate of the cost savings expected from using precision medicine in drug development could reach 17%, potentially saving the industry a significant annual sum of $26 billion worldwide, according to a PwC Strategy report. In fact, researchers in the U.S. found that employing precision molecular diagnostics to cancer, diabetes, heart disease, hypertension, lung disease, and stroke could lead to a minimum of 10% reduction in disease incidence over 50 years, translating to an economic value ranging from $33 billion to $114 billion.” Another report declared the market size for personalized medicine to be $35.7 billion (USD) in 2022, which is projected to increase by 270% by 2027, with 20222027 being the forecast period.
These statistics indicate in that personalized medicine’s economic impact is going to be significant and in many cases beneficial.
II. The Rise of the Translational Bench to Bedside Model in Healthcare
One might surmise how—and why—this rapid rise of a disruptive kind of healthcare took place, and it would mostly lie with the development and successful completion of the sequence of the entire human genome through the efforts of the Human Genome Project. The Human Genome Project, or HGP, was a multi-institutional project directed by the NIH and Celera Genomics to sequence the genome. During the process of sequencing, slow, laborious methods such as positional cloning and Sanger sequencing were employed in the early stages. However, new technologies resulted, such as nextgeneration sequencing, that allowed for faster sequencing times. The outcome was the capability to sequence the entire set of heritable DNA within days as opposed to years and thousands of dollars versus more than a million. Thus began the era of the translational bench-to-bedside model in personalized healthcare and its development and implementation in the broader scientific and medical community. The scientific discovery of the DNA double helix could now effect positive health outcomes in patients by a simple blood draw or cheek swab that could sequence the entire genome, revealing disease risk, DNA mutations, and the presence and absence of genes. This new knowledge from basic science methods could potentially result in data that would potentially be clinically actionable. For example, a patient’s genetic disease could reveal the presence of the BRCA gene, which would indicate breast cancer disease risk.
III. The Roles that Targeted Therapies and Immunotherapies Play in Medicine and Healthcare
The first wave of personalized medicines to be developed could arguably be considered to be Xalkori for non-small cell lung cancer, Gleevec for chronic myeloid leukemia, Kalydeco for cystic f ibrosis, Herceptin for breast cancer, and the immunotherapy Keytruda for a number of solid tumors. Keytruda has been evaluated in a large set of clinical trials called KEYNOTE and has numerous indications, such as for melanoma and non-small cell lung cancer. After extensive research, lung cancers harboring the ALK fusion gene were found to be responsive to Xalkori. Gleevec found wide circulation amongst the popular press as the first drug to target abnormal cancerous cells while sparing normal ones in contrast to strenuous chemotherapy regimens that were prevalent at the time of its development. Targeting the BCR-ABL mutation that became to be known as the Philadelphia chromosome, Gleevec has shown clinical efficacy in chronic myeloid leukemia. Kalydeco targets the G551D mutation, a gene responsible for regulating the transport of salt and water in the body in cystic fibrosis, an autosomal recessive disease that harbors many mutations.
Approximately 4% of cases are positive for the G551D mutation. Herceptin is another targeted therapy that demonstrates the principles of personalized medicine in leading to the retraction of cancer cells while not damaging normal cells. Indicated for breast cancer, Herceptin illustrates the arduous path to approval that many targeted therapies had to undergo through wide-ranging debate and was subject to a high degree of controversy.
A class of cancer therapies arose with the development of precision medicine which mobilized the immune system’s ability to eliminate tumor cells that emerged with a newfound understanding of the tumor microenvironment. These therapies include immune checkpoint inhibitors, chimeric antigen T cell receptor therapies, bispecific antibodies, antibodydrug conjugates and personalized cancer vaccines. Drugs that fall in this category implicate the cancer immunity cycle, which depicts the circular process by which immune cells kill tumor cells. The tumor microenvironment contains both stimulatory and suppressive cells that function to create immune deserts or immune rich regions. These therapies are constructed to harness the immune-rich aspects or stimulatory cells of the complex tumor microenvironment.
IV. Pivotal Clinical Trials
A new category of clinical trials emerged with personalized medicine, which is designed to enrol patients according to the driver mutations they possess that can serve as drug targets. Patients can be enrolled with different cancer types that respond to molecular targets. These are now known as adaptive clinical trials and encompass basket and umbrella trials. Umbrella trials are a category of trials enrolling patients with one cancer type identifying different biomarkers, and then administering different treatments on the basis of the distinguishing biomarkers. Basket trials are trials that include patients with different cancer types who are matched with a therapy that targets the mutations. An example of a basket trial is the I-SPY-1 trial enrolling women with advanced breast cancer tested for targets in estrogen receptor progesterone receptor and HER2. Mammaprint is a companion diagnostic that can predict recurrence. The purpose of I-SPY-1 is to identify molecular biomarkers associated with the disease and determine any clinical response. Biomarkers based on MRI and the endpoint of 3-year disease-free survival were compared with tumor response. Pathologic complete response or no invasive tumor present in breast or lymph nodes was measured, and the results were no less than astounding HR+ patients had the lowest pCR 9%, and HER2+ patients had the highest pCR of 45%. Additionally, the trial also looked for whether pCR could predict recurrence free survival in each molecular subset. This foundational study led to more evidence for integrating molecular data in the form of biomarkers with the capability of providing real-time data for further clinical trials being conducted.
A study reported in a 2024 issue of the New England Journal of Medicine described a phase 2 trial with a basket design. The targeted therapy brigatinib, a (Adapted from Damodaran et al.) selective tyrosine kinase inhibitor, in NF2-driven schwannomatosis with progressive tumors was evaluated. The condition is progressive and there are no approved therapies to date. 40 patients were enrolled and target tumors were 10 vestibular schwannomas, 8 nonvestibular schwannomas and 20 meningiomas and 2 ependymomas; all received briganitib treatment. After a 10.4 month’s follow-up, the percentage of tumors with radiographic response was 10% (95% CI, 3 to 24) for target tumours and 23% (95% CI, 16 to 30) for all tumors, with meningiomas and nonvestibular schwannomas having the greatest benefit.
V. Pharmacogenomics and Mitigating Drug-Related Adverse Events
Pharmacogenomics, also considered a component of personalized medicine, is the study of genetic inheritance in genomic variation that leads to individual variabilities in phenotypic responses to medications. Variation in a single gene or a group of related genes affects the drug pharmacokinetics, of the metabolism and clearance of a drug, or pharmacodynamics, the action or effect of a drug. The key point of pharmacogenomics is the cognizance of genomic variation’s role in drug response is able to mitigate the harmful effects of a drug. The reduction of the adverse drug reaction or ADR is based on what are known as SNPs or single nucleotide polymorphism, which leads to individual variations of genes that are responsible for drug responses. Gene variation can lead to differences in kinetics, enzymatic activity, substrate specificity and stability, and the variability in response is in part due to many processes, including the amount absorbed, route of administration, drug metabolism and speed of elimination.
The crux of the issue is that when there is genomic variation, or variation in the allele specifically, and the enzyme that is produced affects the drug metabolism, the dosage of the drug changes: it either stays the same, increases or decreases depending on which allele the individual has. Poor metabolizers do not metabolize the drug effectively and require a decreased drug dosage to prevent drug toxicity; rapid metabolizers require higher doses than usual for drug efficacy, and extensive metabolizers are wild-type, i.e. they receive normative quantities of the drug. Why these differences in dosage? The individuals contain a different allele of each gene coding for the enzyme, which results in this variation in dosages.
Here are some examples of drug-gene pairs most commonly expressed in the population and their indication:
- Thiopurines (anticancer)—TPMT
- Irinotecan (anticancer)—UGTIA1
- Warfarin (blood thinner)—CYP2C9, CYP4F2, and VKORC1
- Codeine (pain killer)—CYP2D6
- Tamoxifen (anticancer)—CYP2D6
- Clopidogrel (inhibits blood clots)—CYP2C19
CYP2D6 is an example of pharmacogenetics in action. It is a phase II enzyme metabolizing over 100 drugs and environmental toxins. It results in three phenotypes mainly: UM(6%), EM or wild-type (60%), and PM (10%). One example of a drug that CYP2D6 affects is codeine, whose pharmacokinetic profile is affected by the allele’s polymorphism in individuals. Codeine is activated by CYP2D6 to morphine. An EM requires a standard dosage, a PM would require an alternative analgesic agent, and a UM would require lower doses than an EM. Most drugs are inactivated by this enzyme; another example of a prodrug is tamoxifen, the breast cancer agent. Women who are deficient in this enzyme do not respond to tamoxifen and have higher relapse rates.
